


'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Any:
     return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							int				)	->						str:
     __lowerCamelCase  :      Dict														=						{}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          __lowerCamelCase  :      Optional[int]														=						key.replace('heads.cmd.mim_head.cls.predictions' ,       'mmm_image_head'				)
          __lowerCamelCase  :      List[Any]														=						key.replace('heads.cmd.mlm_head.cls.predictions' ,       'mmm_text_head'				)
          __lowerCamelCase  :      Dict														=						key.replace('heads.cmd.itm_head.cls' ,       'itm_head'				)
          __lowerCamelCase  :      List[str]														=						key.replace('heads.cmd.itm_head.pooler' ,       'itm_head.pooler'				)
          __lowerCamelCase  :      Dict														=						key.replace('heads.cmd.clip_head.logit_scale' ,       'flava.logit_scale'				)
          __lowerCamelCase  :      Optional[int]														=						key.replace('heads.fairseq_mlm.cls.predictions' ,       'mlm_head'				)
          __lowerCamelCase  :      str														=						key.replace('heads.imagenet.mim_head.cls.predictions' ,       'mim_head'				)
          __lowerCamelCase  :      List[str]														=						key.replace('mm_text_projection' ,       'flava.text_to_mm_projection'				)
          __lowerCamelCase  :      Union[str, Any]														=						key.replace('mm_image_projection' ,       'flava.image_to_mm_projection'				)
          __lowerCamelCase  :      Optional[int]														=						key.replace('image_encoder.module' ,       'flava.image_model'				)
          __lowerCamelCase  :      Tuple														=						key.replace('text_encoder.module' ,       'flava.text_model'				)
          __lowerCamelCase  :      List[Any]														=						key.replace('mm_encoder.module.encoder.cls_token' ,       'flava.multimodal_model.cls_token'				)
          __lowerCamelCase  :      Union[str, Any]														=						key.replace('mm_encoder.module' ,       'flava.multimodal_model'				)
          __lowerCamelCase  :      str														=						key.replace('text_projection' ,       'flava.text_projection'				)
          __lowerCamelCase  :      int														=						key.replace('image_projection' ,       'flava.image_projection'				)

          __lowerCamelCase  :      str														=						value.float()

     for key, value in codebook_state_dict.items():
          __lowerCamelCase  :      int														=						value

     return upgrade



@torch.no_grad()
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							List[str]=None				)	->						List[str]:





     if config_path is not None:
          __lowerCamelCase  :      List[str]														=						FlavaConfig.from_pretrained(__snake_case				)
     else:
          __lowerCamelCase  :      Optional[int]														=						FlavaConfig()

     __lowerCamelCase  :      Dict														=						FlavaForPreTraining(__snake_case				).eval()

     __lowerCamelCase  :      List[Any]														=						convert_dalle_checkpoint(__snake_case ,       __snake_case ,       save_checkpoint=__snake_case				)

     if os.path.exists(__snake_case				):
          __lowerCamelCase  :      List[str]														=						torch.load(__snake_case ,       map_location='cpu'				)
     else:
          __lowerCamelCase  :      Dict														=						torch.hub.load_state_dict_from_url(__snake_case ,       map_location='cpu'				)

     __lowerCamelCase  :      int														=						upgrade_state_dict(__snake_case ,       __snake_case				)
     hf_model.load_state_dict(__snake_case				)
     __lowerCamelCase  :      Dict														=						hf_model.state_dict()
     __lowerCamelCase  :      List[Any]														=						count_parameters(__snake_case				)
     __lowerCamelCase  :      Tuple														=						count_parameters(__snake_case				) + count_parameters(__snake_case				)

     assert torch.allclose(__snake_case ,       __snake_case ,       atol=1e-3				)

     hf_model.save_pretrained(__snake_case				)


if __name__ == "__main__":
      A__						:   List[str]				=    argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
      parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A__						:   Optional[int]				=    parser.parse_args()

      convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)














"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case : int     		=		{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : str     		=		["""NllbTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : Dict     		=		["""NllbTokenizerFast"""]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb import NllbTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb_fast import NllbTokenizerFast

else:
 import sys

 snake_case : Dict     		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)